SAIL：面向场景的自适应迭代学习用于自动驾驶车辆的长尾轨迹预测
SAIL: Scene-aware Adaptive Iterative Learning for Long-Tail Trajectory Prediction in Autonomous Vehicles
AI总结：
本文提出SAIL框架，通过定义和建模轨迹的三个关键属性维度，结合属性引导增强与自适应对比学习策略，提升自动驾驶车辆在长尾场景下的轨迹预测性能。
摘要：
自动驾驶车辆（AVs）依赖准确的轨迹预测以在多样化的交通环境中安全导航，但现有模型在长尾场景中表现不佳，这些场景包含罕见但安全关键的事件，特征为突发动作、高碰撞风险和复杂交互。这些挑战源于数据不平衡、对长尾轨迹定义不足以及学习策略优先考虑常见行为而非罕见行为。为此，我们提出SAIL，一种新颖的框架，通过系统性地解决长尾问题，首先定义并建模轨迹的三个关键属性维度：预测误差、碰撞风险和状态复杂性。我们的方法随后结合属性引导的增强和特征提取过程与高度自适应的对比学习策略。该策略采用连续余弦动量调度、相似性加权硬负样本挖掘以及基于演进特征聚类的动态伪标签机制。此外，它还结合了聚焦机制以在每个识别的类别中强化学习困难的正样本。这种全面设计使SAIL能够有效识别和预测多样且具有挑战性的长尾事件。在nuScenes和ETH/UCY数据集上的广泛评估表明，SAIL在长尾样本上实现了高达28.8%的预测误差减少，相比最先进的基线，同时在所有场景中保持竞争性的准确性。该框架推进了在现实世界混合自主性环境中的可靠AV轨迹预测。
英文摘要：
Autonomous vehicles (AVs) rely on accurate trajectory prediction for safe navigation in diverse traffic environments, yet existing models struggle with long-tail scenarios-rare but safety-critical events characterized by abrupt maneuvers, high collision risks, and complex interactions. These challenges stem from data imbalance, inadequate definitions of long-tail trajectories, and suboptimal learning strategies that prioritize common behaviors over infrequent ones. To address this, we propose SAIL, a novel framework that systematically tackles the long-tail problem by first defining and modeling trajectories across three key attribute dimensions: prediction error, collision risk, and state complexity. Our approach then synergizes an attribute-guided augmentation and feature extraction process with a highly adaptive contrastive learning strategy. This strategy employs a continuous cosine momentum schedule, similarity-weighted hard-negative mining, and a dynamic pseudo-labeling mechanism based on evolving feature clustering. Furthermore, it incorporates a focusing mechanism to intensify learning on hard-positive samples within each identified class. This comprehensive design enables SAIL to excel at identifying and forecasting diverse and challenging long-tail events. Extensive evaluations on the nuScenes and ETH/UCY datasets demonstrate SAIL's superior performance, achieving up to 28.8% reduction in prediction error on the hardest 1% of long-tail samples compared to state-of-the-art baselines, while maintaining competitive accuracy across all scenarios. This framework advances reliable AV trajectory prediction in real-world, mixed-autonomy settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
